Searching in a Smarter Way

"There’s nothing on TV.” You hardly hear that anymore. In the past few decades, television has exploded from a handful of channels to hundreds. The only downside is deciding what to watch.

Today’s viewers are awash in content options. In addition to all the cable channels, there are apps from networks and sports leagues and over-the-top services like Netflix and Hulu. This has prompted a new refrain: “I can’t decide what to watch.” Viewers are having a tough time wading through this ever-expanding ocean of entertainment.

The obvious solution is to improve content discovery — to make it easier for people to connect to the programs and movies they want and to watch them on whichever device is most convenient at that moment.

But that’s easier said than done. One big obstacle is that most search solutions today still return clumsy results. It’s still too hard for people to find the shows they want by searching for logical keywords like, say, the name of a cast member. They can’t key in or speak the name of their favorite star and then be directed to the latest TV episodes or movies featuring that actor.

The good news is that advances in artificial intelligence (AI) and machine learning are dramatically improving the underlying search algorithms and metadata. This means better search results, personalized recommendations and more targeted results for viewers. Television search engines can now track and categorize your viewing patterns. They know what’s trending and deliver content that’s more relevant and interesting to you.

This is good news not only for viewers but also for studios, broadcasters and networks that want to fully monetize their content catalogs and maximize their revenue. These players are heavily motivated to bring the most compelling content to viewers.

Until now, though, these large, untapped back catalogs have been buried by the content explosion and wild proliferation of distribution channels.

The key to more intuitive search and better discovery is deep, real-time and regionalized metadata. By using enhanced metadata powered by AI and machine learning, entertainment providers can make their entire catalogs more searchable and discoverable. Ultimately, they can increase viewership by presenting more relevant content to their audiences more of the time.

The beauty of enriched metadata and linking is that it can help surface the most relevant content in real time, at just the right moment. It provides discovery systems with a deeper knowledge of entertainment content by identifying relationships between content and keywords — such as Premier League, Manchester United andWayne Rooney — and, most important, understanding the strength or weighting of those connections. Trending data and algorithms enable more contextually relevant discovery by assessing what is happening in the world at any moment and relating that to entertainment content to anticipate what viewers might want next.

Recently, for instance, there was a huge spike in interest around the comedian Jordan Peele, whose satirical horror film Get Out was the highest-grossing original debut in history. A content-discovery engine fueled by machine learning can help viewers connect to the sizable back catalog of Jordan Peele programs, including his appearances on Mad TV, his Comedy Central show, Key & Peele, and his other collaborations with comedian/ Keegan-Michael Key.

REAL TIME TIE-INSEnriched metadata and weighted keywords, combined with entity linking are key to delivering better search results. Another aspect of TiVo’s application of AI and machine learning can be found in the way real-time trending data is used to surface entertainment based on social media and current events. This also enables studios, networks and broadcasters to better monetize their catalogs because their discovery engines know when specific movies, TV shows or celebrities are trending and ensure that the information related to that content is current.

AI and machine learning can also enable conversational search and voice features. This is important because, in today’s ever-more connected world, viewers increasingly expect highly personalized, voice-enabled conversational interfaces with sophisticated learning engines and robust user profiles that can anticipate intent and connect viewers with the desired content using natural language.

Take a sporting event such as the Wimbledon tennis tournament. Viewers should be able to search for matches simply by stating their preference: “Find the Federer match,” for example. Even if the words “Wimbledon” and “tennis” are never mentioned, the system will know that Roger Federer is a tennis champion and that Wimbledon is the highest-profile sporting event at the moment, and thus the optimal search result.

When using a conversational search application, users can ask questions, spoken naturally, and follow-up queries that the technology will understand. This way a user can engage in a normal, free-flowing dialogue, with the voice system responding in much the same manner as an intelligent person in a conversation.

Enriched metadata also leads to greater personalization and convenience. You can say “Reese Witherspoon” and “lawyer,” without naming the actual movie and the discovery engine knows to answer Legally Blonde. Or you can say, “Find the Giants game” and, based on regionalized metadata and the time of year, the system knows you’re looking for a San Francisco Giants baseball game, not a New York Giants football game.

Thanks to advancements in AI and machine learning, studios, broadcasters and networks can now use enhanced metadata to create better entertainment experiences for their viewers. They stand to gain increased revenue — and happier customers — by providing better search, recommendation and voiceenabled discovery features.